Articles | Volume 23, issue 16
https://doi.org/10.5194/acp-23-9071-2023
https://doi.org/10.5194/acp-23-9071-2023
Research article
 | 
19 Sep 2023
Research article |  | 19 Sep 2023

Automated detection and monitoring of methane super-emitters using satellite data

Berend J. Schuit, Joannes D. Maasakkers, Pieter Bijl, Gourav Mahapatra, Anne-Wil van den Berg, Sudhanshu Pandey, Alba Lorente, Tobias Borsdorff, Sander Houweling, Daniel J. Varon, Jason McKeever, Dylan Jervis, Marianne Girard, Itziar Irakulis-Loitxate, Javier Gorroño, Luis Guanter, Daniel H. Cusworth, and Ilse Aben

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Cited articles

ASI – Agenzia Spaziale Italiana (Italian Space Agency): The PRISMA data portal, https://prismauserregistration.asi.it (last access: 20 April, 2023), 2023. a
Bloom, A., Bowman, K., Lee, M., Turner, A., Schroeder, R., Worden, J., Weidner, R., McDonald, K., and Jacob, D.: CMS: Global 0.5-deg Wetland Methane Emissions and Uncertainty (WetCHARTs v1.3.1), ORNL DAAC [data set], https://doi.org/10.3334/ORNLDAAC/1915, 2021. a, b
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Using two machine learning models, which were trained on TROPOMI methane satellite data, we detect 2974 methane plumes, so-called super-emitters, in 2021. We detect methane emissions globally related to urban areas or landfills, coal mining, and oil and gas production. Using our monitoring system, we identify 94 regions with frequent emissions. For 12 locations, we target high-resolution satellite instruments to enlarge and identify the exact infrastructure responsible for the emissions.
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